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Record W2139138955 · doi:10.18235/0009215

Innovation and Productivity in Services: Empirical Evidence from Latin America

2014· preprint· en· W2139138955 on OpenAlexfundno aff
Gustavo Crespi, Ezequiel Tacsir, Fernando Vargas

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
FundersInternational Development Research CentreInter-American Development Bank
KeywordsLatin AmericansProductivityEmpirical evidenceEconomicsBusinessPolitical scienceMacroeconomicsPhilosophyEpistemology

Abstract

fetched live from OpenAlex

This paper analyzes and compares the determinants of innovation in the service industry and its impact on labor productivity at the firm level in three countries of Latin America (Chile, Colombia, and Uruguay). The main findings show that, similar to what is observed in manufacturing industry, service firms that invest the most in innovation activities are more likely to introduce changes or improvements in their production process and/or product mix, and those firms that innovate have higher labor productivity than non-innovative firms. Size was found to be a less relevant determinant of innovation in services than in manufacturing, suggesting that the need for infrastructure and associated sunk costs are lower in services. Conversely, cooperation was found to be far more important for innovation in services than in manufacturing, in line with the more interactive nature of innovation in services. Yet, large differences in statistical significance and size of the coefficients of explanatory variables among the countries studied suggest that the framework conditions where a firm operates have an important role in innovation decisions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.116
GPT teacher head0.287
Teacher spread0.172 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2014
Admission routes1
Has abstractyes

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